AI-Boosted ESG: Transforming Enterprise ESG Performance Through Artificial Intelligence

This dataset is a collection of data and code used in the article AI-Boosted ESG: Transforming Enterprise ESG Performance Through Artificial Intelligence. The hypotheses of this paper include: 1. AI can promote ESG performance; 2.AI can improve ESG performance by improving green technology innovation, labor employment quality and analyst attention, as well as reducing management expense rate; 3. The enhancement effect of AI on ESG performance is more obvious in large-scale enterprises, manufacturing enterprises and enterprises in the eastern region. This dataset includes the three relevant tests above, as well as the relevant procedure codes for several robustness tests, including changing the AI word frequency statistics, using the multi-time-point difference-in-differences model, changing the model type to Tobit model, lagging one stage, and shortening the sample period. The data provided is collated to a certain extent. If you need specific original data or some other related material, you can contact corresponding author Jiayi Yu to ask for it at [e-mail].

本数据集为论文《AI赋能环境、社会和治理（ESG）：通过人工智能重塑企业ESG表现》（AI-Boosted ESG: Transforming Enterprise ESG Performance Through Artificial Intelligence）所使用的数据与代码合集。本文提出的研究假设包括：1. 人工智能可提升企业ESG表现；2. 人工智能可通过推动绿色技术创新、提升劳动就业质量、增强分析师关注度以及降低管理费用率等路径改善企业ESG表现；3. 人工智能对ESG表现的提升效应在大型企业、制造业企业以及东部地区企业中更为显著。本数据集包含上述三项核心检验的相关数据与代码，同时涵盖多项稳健性检验的程序代码，具体包括调整人工智能词频统计方式、采用多时点双重差分模型（difference-in-differences model）、将模型替换为Tobit模型、设置滞后一期项以及缩短样本区间等。本次提供的数据集已完成一定程度的整理工作。若需获取原始数据或其他相关研究材料，可联系通讯作者于佳怡（Jiayi Yu），联系方式为邮箱[e-mail]。

搜集汇总

背景与挑战

背景概述

该数据集为研究人工智能（AI）对中国上市公司ESG绩效影响提供了数据和代码，涵盖2011年至2023年，包含三个核心假设的测试及多个稳健性检验程序。其特点在于聚焦AI通过绿色创新、劳动就业等中介因素提升ESG绩效的机制分析，并特别关注企业规模、行业和地区的异质性影响。